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Found 64 papers, 22 papers with code
Supervised Edge Attention Network for Accurate Image Instance Segmentation
In this task, many works segment instance based on a bounding box from the box head, which means the quality of the detection also affects the completeness of the mask.
MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain Expertise
Recently, large language models (LLMs) have evolved into interactive agents, proficient in planning, tool use, and task execution across a wide variety of tasks.
Enhancing Educational Outcome with Machine Learning: Modeling Friendship Formation, Measuring Peer Effect and Optimizing Class Assignment
We break the problem into three stages (1) friendship prediction (2) peer effect estimation (3) class assignment optimization.
SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior
To tackle this problem, we propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model along with complementary multi-modal data.
CACA Agent: Capability Collaboration based AI Agent
As AI Agents based on Large Language Models (LLMs) have shown potential in practical applications across various fields, how to quickly deploy an AI agent and how to conveniently expand the application scenario of AI agents has become a challenge.
Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations
This is particularly true for medical image segmentation (MIS) datasets, where the processes of collection and fine-grained annotation are time-intensive and laborious.
A$^{3}$lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP
Specifically, our A$^{3}$lign-DFER method is designed with multiple modules that work together to obtain the most suitable expanded-dimensional embeddings for classification and to achieve alignment in three key aspects: affective, dynamic, and bidirectional.
Dynamic Facial Expression Recognition
Facial Expression Recognition
Neural Field Classifiers via Target Encoding and Classification Loss
We successfully propose a novel Neural Field Classifier (NFC) framework which formulates existing neural field methods as classification tasks rather than regression tasks.
HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models
In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems
In this paper, we propose for the first time a novel architecture for online parallel estimation of ads and creatives ranking, as well as the corresponding offline joint optimization model.
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception.
Backdoor Activation Attack: Attack Large Language Models using Activation Steering for Safety-Alignment
Our code and data are available at https://github. com/wang2226/Backdoor-Activation-Attack Warning: this paper contains content that can be offensive or upsetting.
A comprehensive survey on deep active learning in medical image analysis
Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets.
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models
While existing works on claim verification have shown promising results, a crucial piece of the puzzle that remains unsolved is to understand how to verify claims without relying on human-annotated data, which is expensive to create at a large scale.
Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided Refinement
Compared to image duplication and synthesis, image splicing detection is more challenging due to the lack of reference images and the typically small tampered areas.
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout
Here, we propose a goal-conditioned HRL framework named Guided Cooperation via Model-based Rollout (GCMR), aiming to bridge inter-layer information synchronization and cooperation by exploiting forward dynamics.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images.
Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation
Fact-checking in financial domain is under explored, and there is a shortage of quality dataset in this domain.
Investigating Online Financial Misinformation and Its Consequences: A Computational Perspective
In conclusion, this research paper sheds light on the pervasive issue of online financial misinformation and its wide-ranging consequences.
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning
In this work, we propose an efficient computation framework for multimodal alignment by introducing a novel visual semantic module to further improve the performance of the VL tasks.
S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields
Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images.
LOIS: Looking Out of Instance Semantics for Visual Question Answering
Visual question answering (VQA) has been intensively studied as a multimodal task that requires effort in bridging vision and language to infer answers correctly.
Free-Form Composition Networks for Egocentric Action Recognition
We evaluated our method on three popular egocentric action recognition datasets, Something-Something V2, H2O, and EPIC-KITCHENS-100, and the experimental results demonstrate the effectiveness of the proposed method for handling data scarcity problems, including long-tailed and few-shot egocentric action recognition.
Hierarchical Matching and Reasoning for Multi-Query Image Retrieval
Afterwards, the VR module is developed to excavate the potential semantic correlations among multiple region-query pairs, which further explores the high-level reasoning similarity.
Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation
Existing knowledge distillation works for semantic segmentation mainly focus on transferring high-level contextual knowledge from teacher to student.
ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about
Large language models have gained considerable interest for their impressive performance on various tasks.
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning.
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)
In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis.
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
MIC significantly improves the state-of-the-art performance across the different recognition tasks for synthetic-to-real, day-to-nighttime, and clear-to-adverse-weather UDA.
Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues
Because of the uniqueness and importance of combating health misinformation in social media, we conduct this survey to further facilitate interdisciplinary research on this problem.
The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)
The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts.
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval
We introduce dynamic dictionaries for both modalities to enlarge the scale of image-text pairs, and diversity-sensitiveness is achieved by adaptive negative pair weighting.
SoMoFormer: Social-Aware Motion Transformer for Multi-Person Motion Prediction
Most prior methods only involve learning local pose dynamics for individual motion (without global body trajectory) and also struggle to capture complex interaction dependencies for social interactions.
Positively transitioned sentiment dialogue corpus for developing emotion-affective open-domain chatbots
In this paper, we describe a data enhancement method for developing Emily, an emotion-affective open-domain chatbot.
Boosting Video-Text Retrieval with Explicit High-Level Semantics
Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding, which aims to search for relevant video (text) given a query (video).
Temporal Saliency Query Network for Efficient Video Recognition
For each category, the common pattern of it is employed as a query and the most salient frames are responded to it.
Ranked #5 on
Action Recognition
on ActivityNet
NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition
On the video level, a temporal attention module is learned under dual video-level supervisions on both the salient and the non-salient representations.
Ranked #4 on
Action Recognition
on ActivityNet
Can multi-label classification networks know what they don't know?
Our results show consistent improvement over previous methods that are based on the maximum-valued scores, which fail to capture joint information from multiple labels.
Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona
In this paper, we describe approaches for developing Emily, an emotion-affective open-domain chatbot.
Efficient and Systematic Partitioning of Large and Deep Neural Networks for Parallelization
We propose the Flex-Edge Recursive Graph and the Double Recursive Algorithm, successfully limiting our parallelization strategy generation to a linear complexity with a good quality of parallelization strategy.
Freeing Hybrid Distributed AI Training Configuration
Hybrid Parallelism (HP), which applies different parallel strategies on different parts of DNNs, is more efficient but requires advanced configurations.
CONet: Channel Optimization for Convolutional Neural Networks
To solve this, we introduce an efficient dynamic scaling algorithm -- CONet -- that automatically optimizes channel sizes across network layers for a given CNN.
Step-Wise Hierarchical Alignment Network for Image-Text Matching
Image-text matching plays a central role in bridging the semantic gap between vision and language.
Can multi-label classification networks know what they don’t know?
Our results show consistent improvement over previous methods that are based on the maximum-valued scores, which fail to capture joint information from multiple labels.
Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning
Machine Learning (ML) has been embraced as a powerful tool by the financial industry, with notable applications spreading in various domains including investment management.
Hierarchical Cost Analysis for Distributed DL
In order to formalize the behaviors of the HP in distributed DL and quantitatively evaluate the cost caused by HP, we are studying Bridging DL composed by a double-level execution model associated with a symbolic cost model.
Strictly Decentralized Adaptive Estimation of External Fields using Reproducing Kernels
Approximations of the evolution of the ideal local estimate $\hat{g}^i_t$ of agent $i$ is constructed solely using observations made by agent $i$ on a fine time scale.
Energy-based Out-of-distribution Detection for Multi-label Classification
Our results show consistent improvement over previous methods that are based on the maximum-valued scores, which fail to capture joint information from multiple labels.
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning
Query reformulation aims to alter noisy or ambiguous text sequences into coherent ones closer to natural language questions.
Context-Aware Graph Convolution Network for Target Re-identification
Most existing re-identification methods focus on learning robust and discriminative features with deep convolution networks.
Frame Aggregation and Multi-Modal Fusion Framework for Video-Based Person Recognition
To tackle the challenges above, we propose a novel Frame Aggregation and Multi-Modal Fusion (FAMF) framework for video-based person recognition, which aggregates face features and incorporates them with multi-modal information to identify persons in videos.
Learning Risk Preferences from Investment Portfolios Using Inverse Optimization
Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data.
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains.
Ranked #15 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
In this paper, we propose a Consensus-aware Visual-Semantic Embedding (CVSE) model to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
Large scale continuous-time mean-variance portfolio allocation via reinforcement learning
We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL).
Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework
We approach the continuous-time mean-variance (MV) portfolio selection with reinforcement learning (RL).
Saliency-Guided Attention Network for Image-Sentence Matching
Concretely, the saliency detector provides the visual saliency information as the guidance for the two attention modules.
Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings
Recent studies show that the Dirichlet Multinomial Mixture (DMM) model is effective for topic inference over short texts by assuming that each piece of short text is generated by a single topic.
Exploration versus exploitation in reinforcement learning: a stochastic control approach
We carry out a complete analysis of the problem in the linear--quadratic (LQ) setting and deduce that the optimal feedback control distribution for balancing exploitation and exploration is Gaussian.
Parameter-Free Spatial Attention Network for Person Re-Identification
We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model.
Ranked #20 on
Person Re-Identification
on DukeMTMC-reID
Multi-task Sparse Learning with Beta Process Prior for Action Recognition
In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible.
